package com.at.bigdata.spark.streaming

import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
 *
 * @author cdhuangchao3
 * @date 2023/5/29 9:24 PM
 */
object SparkStreaming11_Req1 {

  def main(args: Array[String]): Unit = {
    // 创建环境
    // 创建时，需要传递2个参数：
    //    param： 环境配置
    val sc = new SparkConf().setMaster("local[*]").setAppName("operator")
    //    param2: 采集周期
    val ssc = new StreamingContext(sc, Seconds(3))

    val kafkaPara = Map[String, Object](
      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> "linux1:9092,linux2:9092,linux3:9092",
      ConsumerConfig.GROUP_ID_CONFIG -> "at",
      ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> "org.apache.kafka.common.serialization.StringDeserializer",
      ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> "org.apache.kafka.common.serialization.StringDeserializer"
    )

    val kafkaDataDS = KafkaUtils.createDirectStream[String, String](
      ssc,
      LocationStrategies.PreferConsistent,
      ConsumerStrategies.Subscribe[String, String](Set("at"), kafkaPara)
    )

    kafkaDataDS.map(_.value()).print()

    // 1、启动采集器
    ssc.start()
    // 2、等待采集器的关闭
    ssc.awaitTermination()
  }

}
